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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (10): 750-760    DOI: 10.1631/jzus.C12a0049
    
Transit smart card data mining for passenger origin information extraction
Xiao-lei Ma, Yin-hai Wang, Feng Chen, Jian-feng Liu
Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, USA; Beijing Transportation Research Center, Beijing 100073, China
Transit smart card data mining for passenger origin information extraction
Xiao-lei Ma, Yin-hai Wang, Feng Chen, Jian-feng Liu
Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, USA; Beijing Transportation Research Center, Beijing 100073, China
 全文: PDF 
摘要： The automated fare collection (AFC) system, also known as the transit smart card (SC) system, has gained more and more popularity among transit agencies worldwide. Compared with the conventional manual fare collection system, an AFC system has its inherent advantages in low labor cost and high efficiency for fare collection and transaction data archival. Although it is possible to collect highly valuable data from transit SC transactions, substantial efforts and methodologies are needed for extracting such data because most AFC systems are not initially designed for data collection. This is true especially for the Beijing AFC system, where a passenger’s boarding stop (origin) on a flat-rate bus is not recorded on the check-in scan. To extract passengers’ origin data from recorded SC transaction information, a Markov chain based Bayesian decision tree algorithm is developed in this study. Using the time invariance property of the Markov chain, the algorithm is further optimized and simplified to have a linear computational complexity. This algorithm is verified with transit vehicles equipped with global positioning system (GPS) data loggers. Our verification results demonstrated that the proposed algorithm is effective in extracting transit passengers’ origin information from SC transactions with a relatively high accuracy. Such transit origin data are highly valuable for transit system planning and route optimization.
关键词： Transit smart card;  Automated fare collection (AFC);  Bayesian decision tree;  Markov chain;  Origin inference    
Abstract: The automated fare collection (AFC) system, also known as the transit smart card (SC) system, has gained more and more popularity among transit agencies worldwide. Compared with the conventional manual fare collection system, an AFC system has its inherent advantages in low labor cost and high efficiency for fare collection and transaction data archival. Although it is possible to collect highly valuable data from transit SC transactions, substantial efforts and methodologies are needed for extracting such data because most AFC systems are not initially designed for data collection. This is true especially for the Beijing AFC system, where a passenger’s boarding stop (origin) on a flat-rate bus is not recorded on the check-in scan. To extract passengers’ origin data from recorded SC transaction information, a Markov chain based Bayesian decision tree algorithm is developed in this study. Using the time invariance property of the Markov chain, the algorithm is further optimized and simplified to have a linear computational complexity. This algorithm is verified with transit vehicles equipped with global positioning system (GPS) data loggers. Our verification results demonstrated that the proposed algorithm is effective in extracting transit passengers’ origin information from SC transactions with a relatively high accuracy. Such transit origin data are highly valuable for transit system planning and route optimization.
Key words: Transit smart card    Automated fare collection (AFC)    Bayesian decision tree    Markov chain    Origin inference
收稿日期: 2012-02-23 出版日期: 2012-10-01
CLC:  U121  
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引用本文:

Xiao-lei Ma, Yin-hai Wang, Feng Chen, Jian-feng Liu. Transit smart card data mining for passenger origin information extraction. Front. Inform. Technol. Electron. Eng., 2012, 13(10): 750-760.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C12a0049    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I10/750

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